@inproceedings{srinivasan2020code-mixed,
author = {Srinivasan, Anirudh and Dandapat, Sandipan and Choudhury, Monojit},
title = {Code-mixed parse trees and how to find them},
booktitle = {Workshop on Computational Approaches to Code Switching},
year = {2020},
month = {May},
abstract = {In this paper, we explore the methods of obtaining parse trees of code-mixed sentences and analyse the obtained trees. Existing work has shown that linguistic theories can be used to generate code-mixed sentences from a set of parallel sentences. We build upon this work, using one of these theories, the Equivalence-Constraint theory to obtain the parse trees of synthetically generated code-mixed sentences and evaluate them with a neural constituency parser. We highlight the lack of a dataset non-synthetic code-mixed constituency parse trees and how it makes our evaluation difficult. To complete our evaluation, we convert a code-mixed dependency parse tree set into “pseudo constituency trees” and find that a parser trained on synthetically generated trees is able to decently parse these as well.},
publisher = {European Language Resources Association},
url = {http://approjects.co.za/?big=en-us/research/publication/code-mixed-parse-trees-and-how-to-find-them/},
pages = {57-64},
}